Pandas Indexes & Sorting

Index Basics

In [2]:

import pandas as pdbtc = pd.read_csv("data/coin_Bitcoin.csv")

In [3]:

btc

Out[3]:

 

SNo

Name

Symbol

Date

High

Low

Open

Close

Volume

Marketcap

0

1

Bitcoin

BTC

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2

Bitcoin

BTC

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

3

Bitcoin

BTC

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

4

Bitcoin

BTC

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

5

Bitcoin

BTC

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

...

2986

2987

Bitcoin

BTC

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2988

Bitcoin

BTC

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2989

Bitcoin

BTC

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2990

Bitcoin

BTC

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2991

Bitcoin

BTC

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 10 columns

In [4]:

btc.set_index("Date")

Out[4]:

 

SNo

Name

Symbol

High

Low

Open

Close

Volume

Marketcap

Date

 

 

 

 

 

 

 

 

 

2013-04-29 23:59:59

1

Bitcoin

BTC

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

2013-04-30 23:59:59

2

Bitcoin

BTC

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2013-05-01 23:59:59

3

Bitcoin

BTC

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

2013-05-02 23:59:59

4

Bitcoin

BTC

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

2013-05-03 23:59:59

5

Bitcoin

BTC

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

2021-07-02 23:59:59

2987

Bitcoin

BTC

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2021-07-03 23:59:59

2988

Bitcoin

BTC

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2021-07-04 23:59:59

2989

Bitcoin

BTC

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2021-07-05 23:59:59

2990

Bitcoin

BTC

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2021-07-06 23:59:59

2991

Bitcoin

BTC

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 9 columns

In [5]:

btc

Out[5]:

 

SNo

Name

Symbol

Date

High

Low

Open

Close

Volume

Marketcap

0

1

Bitcoin

BTC

2013-04-29 23:59:59

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

1

2

Bitcoin

BTC

2013-04-30 23:59:59

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2

3

Bitcoin

BTC

2013-05-01 23:59:59

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

3

4

Bitcoin

BTC

2013-05-02 23:59:59

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

4

5

Bitcoin

BTC

2013-05-03 23:59:59

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

...

2986

2987

Bitcoin

BTC

2021-07-02 23:59:59

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2987

2988

Bitcoin

BTC

2021-07-03 23:59:59

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2988

2989

Bitcoin

BTC

2021-07-04 23:59:59

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2989

2990

Bitcoin

BTC

2021-07-05 23:59:59

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2990

2991

Bitcoin

BTC

2021-07-06 23:59:59

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 10 columns

In [6]:

btc.set_index("Date", inplace=True)

In [7]:

btc

Out[7]:

 

SNo

Name

Symbol

High

Low

Open

Close

Volume

Marketcap

Date

 

 

 

 

 

 

 

 

 

2013-04-29 23:59:59

1

Bitcoin

BTC

147.488007

134.000000

134.444000

144.539993

0.000000e+00

1.603769e+09

2013-04-30 23:59:59

2

Bitcoin

BTC

146.929993

134.050003

144.000000

139.000000

0.000000e+00

1.542813e+09

2013-05-01 23:59:59

3

Bitcoin

BTC

139.889999

107.720001

139.000000

116.989998

0.000000e+00

1.298955e+09

2013-05-02 23:59:59

4

Bitcoin

BTC

125.599998

92.281898

116.379997

105.209999

0.000000e+00

1.168517e+09

2013-05-03 23:59:59

5

Bitcoin

BTC

108.127998

79.099998

106.250000

97.750000

0.000000e+00

1.085995e+09

...

...

...

...

...

...

...

...

...

...

2021-07-02 23:59:59

2987

Bitcoin

BTC

33939.588699

32770.680780

33549.600177

33897.048590

3.872897e+10

6.354508e+11

2021-07-03 23:59:59

2988

Bitcoin

BTC

34909.259899

33402.696536

33854.421362

34668.548402

2.438396e+10

6.499397e+11

2021-07-04 23:59:59

2989

Bitcoin

BTC

35937.567147

34396.477458

34665.564866

35287.779766

2.492431e+10

6.615748e+11

2021-07-05 23:59:59

2990

Bitcoin

BTC

35284.344430

33213.661034

35284.344430

33746.002456

2.672155e+10

6.326962e+11

2021-07-06 23:59:59

2991

Bitcoin

BTC

35038.536363

33599.916169

33723.509655

34235.193451

2.650126e+10

6.418992e+11

2991 rows × 9 columns

In [8]:

btc.index

Out[8]:

Index(['2013-04-29 23:59:59', '2013-04-30 23:59:59', '2013-05-01 23:59:59',

       '2013-05-02 23:59:59', '2013-05-03 23:59:59', '2013-05-04 23:59:59',

       '2013-05-05 23:59:59', '2013-05-06 23:59:59', '2013-05-07 23:59:59',

       '2013-05-08 23:59:59',

       ...

       '2021-06-27 23:59:59', '2021-06-28 23:59:59', '2021-06-29 23:59:59',

       '2021-06-30 23:59:59', '2021-07-01 23:59:59', '2021-07-02 23:59:59',

       '2021-07-03 23:59:59', '2021-07-04 23:59:59', '2021-07-05 23:59:59',

       '2021-07-06 23:59:59'],

      dtype='object', name='Date', length=2991)

In [9]:

btc.High

Out[9]:

Date

2013-04-29 23:59:59      147.488007

2013-04-30 23:59:59      146.929993

2013-05-01 23:59:59      139.889999

2013-05-02 23:59:59      125.599998

2013-05-03 23:59:59      108.127998

                           ...     

2021-07-02 23:59:59    33939.588699

2021-07-03 23:59:59    34909.259899

2021-07-04 23:59:59    35937.567147

2021-07-05 23:59:59    35284.344430

2021-07-06 23:59:59    35038.536363

Name: High, Length: 2991, dtype: float64

In [10]:

btc.High.plot()

Out[10]:

<AxesSubplot:xlabel='Date'>

 

In [11]:

btc = pd.read_csv("data/coin_Bitcoin.csv")btc.High.plot()

Out[11]:

<AxesSubplot:>

 

In [12]:

countries = pd.read_csv("data/world-happiness-report-2021.csv")

In [73]:

countries["Healthy life expectancy"]

Out[73]:

Country name

Afghanistan    52.493

Albania        68.999

Algeria        66.005

Argentina      69.000

Armenia        67.055

                ...  

Venezuela      66.700

Vietnam        68.034

Yemen          57.122

Zambia         55.809

Zimbabwe       56.201

Name: Healthy life expectancy, Length: 149, dtype: float64

In [14]:

countries

Out[14]:

 

Country name

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

0

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

1

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

2

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

3

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

4

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

144

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

145

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

146

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

147

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

148

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 20 columns

In [15]:

countries.set_index("Country name", inplace=True)

In [16]:

countries

Out[16]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [17]:

countries["Healthy life expectancy"]

Out[17]:

Country name

Finland        72.000

Denmark        72.700

Switzerland    74.400

Iceland        73.000

Netherlands    72.400

                ...  

Lesotho        48.700

Botswana       59.269

Rwanda         61.400

Zimbabwe       56.201

Afghanistan    52.493

Name: Healthy life expectancy, Length: 149, dtype: float64

In [18]:

countries["Healthy life expectancy"].head(5).plot()

Out[18]:

<AxesSubplot:xlabel='Country name'>

 

In [19]:

df = pd.read_csv("data/world-happiness-report-2021.csv", index_col=0)

In [20]:

df

Out[20]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [21]:

df["Ladder score"]

Out[21]:

Country name

Finland        7.842

Denmark        7.620

Switzerland    7.571

Iceland        7.554

Netherlands    7.464

               ...  

Lesotho        3.512

Botswana       3.467

Rwanda         3.415

Zimbabwe       3.145

Afghanistan    2.523

Name: Ladder score, Length: 149, dtype: float64

In [22]:

countries

Out[22]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

Sorting

In [23]:

countries.sort_values("Healthy life expectancy")

Out[23]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chad

Sub-Saharan Africa

4.355

0.094

4.540

4.171

7.364

0.619

48.478

0.579

0.041

0.807

2.43

0.255

0.353

0.000

0.240

0.215

0.084

3.209

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Nigeria

Sub-Saharan Africa

4.759

0.052

4.861

4.658

8.533

0.740

50.102

0.737

0.037

0.878

2.43

0.663

0.625

0.051

0.433

0.212

0.039

2.736

Ivory Coast

Sub-Saharan Africa

5.306

0.078

5.460

5.152

8.551

0.644

50.114

0.741

-0.016

0.794

2.43

0.669

0.409

0.052

0.438

0.177

0.092

3.469

Swaziland

Sub-Saharan Africa

4.308

0.071

4.448

4.168

9.065

0.770

50.833

0.647

-0.185

0.708

2.43

0.849

0.693

0.074

0.323

0.067

0.147

2.155

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Spain

Western Europe

6.491

0.042

6.574

6.408

10.571

0.932

74.700

0.761

-0.081

0.745

2.43

1.375

1.057

0.826

0.462

0.135

0.124

2.513

Japan

East Asia

5.940

0.040

6.020

5.861

10.611

0.884

75.100

0.796

-0.258

0.638

2.43

1.389

0.949

0.838

0.504

0.020

0.192

2.048

Hong Kong S.A.R. of China

East Asia

5.477

0.049

5.573

5.380

11.000

0.836

76.820

0.717

0.067

0.403

2.43

1.525

0.841

0.893

0.408

0.232

0.342

1.236

Singapore

Southeast Asia

6.377

0.043

6.460

6.293

11.488

0.915

76.953

0.927

-0.018

0.082

2.43

1.695

1.019

0.897

0.664

0.176

0.547

1.379

149 rows × 19 columns

In [24]:

countries.sort_values("Healthy life expectancy", ascending=False)

Out[24]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Singapore

Southeast Asia

6.377

0.043

6.460

6.293

11.488

0.915

76.953

0.927

-0.018

0.082

2.43

1.695

1.019

0.897

0.664

0.176

0.547

1.379

Hong Kong S.A.R. of China

East Asia

5.477

0.049

5.573

5.380

11.000

0.836

76.820

0.717

0.067

0.403

2.43

1.525

0.841

0.893

0.408

0.232

0.342

1.236

Japan

East Asia

5.940

0.040

6.020

5.861

10.611

0.884

75.100

0.796

-0.258

0.638

2.43

1.389

0.949

0.838

0.504

0.020

0.192

2.048

Spain

Western Europe

6.491

0.042

6.574

6.408

10.571

0.932

74.700

0.761

-0.081

0.745

2.43

1.375

1.057

0.826

0.462

0.135

0.124

2.513

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Swaziland

Sub-Saharan Africa

4.308

0.071

4.448

4.168

9.065

0.770

50.833

0.647

-0.185

0.708

2.43

0.849

0.693

0.074

0.323

0.067

0.147

2.155

Ivory Coast

Sub-Saharan Africa

5.306

0.078

5.460

5.152

8.551

0.644

50.114

0.741

-0.016

0.794

2.43

0.669

0.409

0.052

0.438

0.177

0.092

3.469

Nigeria

Sub-Saharan Africa

4.759

0.052

4.861

4.658

8.533

0.740

50.102

0.737

0.037

0.878

2.43

0.663

0.625

0.051

0.433

0.212

0.039

2.736

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Chad

Sub-Saharan Africa

4.355

0.094

4.540

4.171

7.364

0.619

48.478

0.579

0.041

0.807

2.43

0.255

0.353

0.000

0.240

0.215

0.084

3.209

149 rows × 19 columns

In [25]:

countries

Out[25]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

Netherlands

Western Europe

7.464

0.027

7.518

7.410

10.932

0.942

72.400

0.913

0.175

0.338

2.43

1.501

1.079

0.753

0.647

0.302

0.384

2.798

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Botswana

Sub-Saharan Africa

3.467

0.074

3.611

3.322

9.782

0.784

59.269

0.824

-0.246

0.801

2.43

1.099

0.724

0.340

0.539

0.027

0.088

0.648

Rwanda

Sub-Saharan Africa

3.415

0.068

3.548

3.282

7.676

0.552

61.400

0.897

0.061

0.167

2.43

0.364

0.202

0.407

0.627

0.227

0.493

1.095

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [26]:

countries.sort_values("Healthy life expectancy", ascending=False, inplace=True)

In [27]:

countries

Out[27]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Singapore

Southeast Asia

6.377

0.043

6.460

6.293

11.488

0.915

76.953

0.927

-0.018

0.082

2.43

1.695

1.019

0.897

0.664

0.176

0.547

1.379

Hong Kong S.A.R. of China

East Asia

5.477

0.049

5.573

5.380

11.000

0.836

76.820

0.717

0.067

0.403

2.43

1.525

0.841

0.893

0.408

0.232

0.342

1.236

Japan

East Asia

5.940

0.040

6.020

5.861

10.611

0.884

75.100

0.796

-0.258

0.638

2.43

1.389

0.949

0.838

0.504

0.020

0.192

2.048

Spain

Western Europe

6.491

0.042

6.574

6.408

10.571

0.932

74.700

0.761

-0.081

0.745

2.43

1.375

1.057

0.826

0.462

0.135

0.124

2.513

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Swaziland

Sub-Saharan Africa

4.308

0.071

4.448

4.168

9.065

0.770

50.833

0.647

-0.185

0.708

2.43

0.849

0.693

0.074

0.323

0.067

0.147

2.155

Ivory Coast

Sub-Saharan Africa

5.306

0.078

5.460

5.152

8.551

0.644

50.114

0.741

-0.016

0.794

2.43

0.669

0.409

0.052

0.438

0.177

0.092

3.469

Nigeria

Sub-Saharan Africa

4.759

0.052

4.861

4.658

8.533

0.740

50.102

0.737

0.037

0.878

2.43

0.663

0.625

0.051

0.433

0.212

0.039

2.736

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Chad

Sub-Saharan Africa

4.355

0.094

4.540

4.171

7.364

0.619

48.478

0.579

0.041

0.807

2.43

0.255

0.353

0.000

0.240

0.215

0.084

3.209

149 rows × 19 columns

In [28]:

houses = pd.read_csv("data/kc_house_data.csv")titanic = pd.read_csv("data/titanic.csv")

In [29]:

houses.sort_values("price", ascending=False)

Out[29]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

7252

6762700020

20141013T000000

7700000.0

6

8.00

12050

27600

2.5

0

3

...

13

8570

3480

1910

1987

98102

47.6298

-122.323

3940

8800

3914

9808700762

20140611T000000

7062500.0

5

4.50

10040

37325

2.0

1

2

...

11

7680

2360

1940

2001

98004

47.6500

-122.214

3930

25449

9254

9208900037

20140919T000000

6885000.0

6

7.75

9890

31374

2.0

0

4

...

13

8860

1030

2001

0

98039

47.6305

-122.240

4540

42730

4411

2470100110

20140804T000000

5570000.0

5

5.75

9200

35069

2.0

0

0

...

13

6200

3000

2001

0

98039

47.6289

-122.233

3560

24345

1448

8907500070

20150413T000000

5350000.0

5

5.00

8000

23985

2.0

0

4

...

12

6720

1280

2009

0

98004

47.6232

-122.220

4600

21750

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

8274

3883800011

20141105T000000

82000.0

3

1.00

860

10426

1.0

0

0

...

6

860

0

1954

0

98146

47.4987

-122.341

1140

11250

16198

3028200080

20150324T000000

81000.0

2

1.00

730

9975

1.0

0

0

...

5

730

0

1943

0

98168

47.4808

-122.315

860

9000

465

8658300340

20140523T000000

80000.0

1

0.75

430

5050

1.0

0

0

...

4

430

0

1912

0

98014

47.6499

-121.909

1200

7500

15293

40000362

20140506T000000

78000.0

2

1.00

780

16344

1.0

0

0

...

5

780

0

1942

0

98168

47.4739

-122.280

1700

10387

1149

3421079032

20150217T000000

75000.0

1

0.00

670

43377

1.0

0

0

...

3

670

0

1966

0

98022

47.2638

-121.906

1160

42882

21613 rows × 21 columns

In [30]:

houses.sort_values("bedrooms", ascending=False)

Out[30]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

15870

2402100895

20140625T000000

640000.0

33

1.75

1620

6000

1.0

0

0

...

7

1040

580

1947

0

98103

47.6878

-122.331

1330

4700

8757

1773100755

20140821T000000

520000.0

11

3.00

3000

4960

2.0

0

0

...

7

2400

600

1918

1999

98106

47.5560

-122.363

1420

4960

15161

5566100170

20141029T000000

650000.0

10

2.00

3610

11914

2.0

0

0

...

7

3010

600

1958

0

98006

47.5705

-122.175

2040

11914

13314

627300145

20140814T000000

1148000.0

10

5.25

4590

10920

1.0

0

2

...

9

2500

2090

2008

0

98004

47.5861

-122.113

2730

10400

19254

8812401450

20141229T000000

660000.0

10

3.00

2920

3745

2.0

0

0

...

7

1860

1060

1913

0

98105

47.6635

-122.320

1810

3745

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

19452

3980300371

20140926T000000

142000.0

0

0.00

290

20875

1.0

0

0

...

1

290

0

1963

0

98024

47.5308

-121.888

1620

22850

8484

2310060040

20140925T000000

240000.0

0

2.50

1810

5669

2.0

0

0

...

7

1810

0

2003

0

98038

47.3493

-122.053

1810

5685

875

6306400140

20140612T000000

1095000.0

0

0.00

3064

4764

3.5

0

2

...

7

3064

0

1990

0

98102

47.6362

-122.322

2360

4000

8477

2569500210

20141117T000000

339950.0

0

2.50

2290

8319

2.0

0

0

...

8

2290

0

1985

0

98042

47.3473

-122.151

2500

8751

9773

3374500520

20150429T000000

355000.0

0

0.00

2460

8049

2.0

0

0

...

8

2460

0

1990

0

98031

47.4095

-122.168

2520

8050

21613 rows × 21 columns

In [31]:

houses.sort_values(["bedrooms", "bathrooms"], ascending=False)

Out[31]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

15870

2402100895

20140625T000000

640000.0

33

1.75

1620

6000

1.0

0

0

...

7

1040

580

1947

0

98103

47.6878

-122.331

1330

4700

8757

1773100755

20140821T000000

520000.0

11

3.00

3000

4960

2.0

0

0

...

7

2400

600

1918

1999

98106

47.5560

-122.363

1420

4960

13314

627300145

20140814T000000

1148000.0

10

5.25

4590

10920

1.0

0

2

...

9

2500

2090

2008

0

98004

47.5861

-122.113

2730

10400

19254

8812401450

20141229T000000

660000.0

10

3.00

2920

3745

2.0

0

0

...

7

1860

1060

1913

0

98105

47.6635

-122.320

1810

3745

15161

5566100170

20141029T000000

650000.0

10

2.00

3610

11914

2.0

0

0

...

7

3010

600

1958

0

98006

47.5705

-122.175

2040

11914

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

6994

2954400190

20140624T000000

1295650.0

0

0.00

4810

28008

2.0

0

0

...

12

4810

0

1990

0

98053

47.6642

-122.069

4740

35061

9773

3374500520

20150429T000000

355000.0

0

0.00

2460

8049

2.0

0

0

...

8

2460

0

1990

0

98031

47.4095

-122.168

2520

8050

9854

7849202190

20141223T000000

235000.0

0

0.00

1470

4800

2.0

0

0

...

7

1470

0

1996

0

98065

47.5265

-121.828

1060

7200

14423

9543000205

20150413T000000

139950.0

0

0.00

844

4269

1.0

0

0

...

7

844

0

1913

0

98001

47.2781

-122.250

1380

9600

19452

3980300371

20140926T000000

142000.0

0

0.00

290

20875

1.0

0

0

...

1

290

0

1963

0

98024

47.5308

-121.888

1620

22850

21613 rows × 21 columns

In [32]:

houses.sort_values(["bathrooms", "bedrooms"], ascending=False)

Out[32]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

12777

1225069038

20140505T000000

2280000.0

7

8.00

13540

307752

3.0

0

4

...

12

9410

4130

1999

0

98053

47.6675

-121.986

4850

217800

7252

6762700020

20141013T000000

7700000.0

6

8.00

12050

27600

2.5

0

3

...

13

8570

3480

1910

1987

98102

47.6298

-122.323

3940

8800

9254

9208900037

20140919T000000

6885000.0

6

7.75

9890

31374

2.0

0

4

...

13

8860

1030

2001

0

98039

47.6305

-122.240

4540

42730

8546

424049043

20140811T000000

450000.0

9

7.50

4050

6504

2.0

0

0

...

7

4050

0

1996

0

98144

47.5923

-122.301

1448

3866

4024

9175600025

20141007T000000

800000.0

7

6.75

7480

41664

2.0

0

2

...

11

5080

2400

1953

0

98166

47.4643

-122.368

2810

33190

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

6994

2954400190

20140624T000000

1295650.0

0

0.00

4810

28008

2.0

0

0

...

12

4810

0

1990

0

98053

47.6642

-122.069

4740

35061

9773

3374500520

20150429T000000

355000.0

0

0.00

2460

8049

2.0

0

0

...

8

2460

0

1990

0

98031

47.4095

-122.168

2520

8050

9854

7849202190

20141223T000000

235000.0

0

0.00

1470

4800

2.0

0

0

...

7

1470

0

1996

0

98065

47.5265

-121.828

1060

7200

14423

9543000205

20150413T000000

139950.0

0

0.00

844

4269

1.0

0

0

...

7

844

0

1913

0

98001

47.2781

-122.250

1380

9600

19452

3980300371

20140926T000000

142000.0

0

0.00

290

20875

1.0

0

0

...

1

290

0

1963

0

98024

47.5308

-121.888

1620

22850

21613 rows × 21 columns

In [33]:

titanic

Out[33]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

0

1

1

Allen, Miss. Elisabeth Walton

female

29

0

0

24160

211.3375

B5

S

2

?

St Louis, MO

1

1

1

Allison, Master. Hudson Trevor

male

0.9167

1

2

113781

151.55

C22 C26

S

11

?

Montreal, PQ / Chesterville, ON

2

1

0

Allison, Miss. Helen Loraine

female

2

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

3

1

0

Allison, Mr. Hudson Joshua Creighton

male

30

1

2

113781

151.55

C22 C26

S

?

135

Montreal, PQ / Chesterville, ON

4

1

0

Allison, Mrs. Hudson J C (Bessie Waldo Daniels)

female

25

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1304

3

0

Zabour, Miss. Hileni

female

14.5

1

0

2665

14.4542

?

C

?

328

?

1305

3

0

Zabour, Miss. Thamine

female

?

1

0

2665

14.4542

?

C

?

?

?

1306

3

0

Zakarian, Mr. Mapriededer

male

26.5

0

0

2656

7.225

?

C

?

304

?

1307

3

0

Zakarian, Mr. Ortin

male

27

0

0

2670

7.225

?

C

?

?

?

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

1309 rows × 14 columns

In [34]:

titanic.sort_values("name")

Out[34]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

600

3

0

Abbing, Mr. Anthony

male

42

0

0

C.A. 5547

7.55

?

S

?

?

?

601

3

0

Abbott, Master. Eugene Joseph

male

13

0

2

C.A. 2673

20.25

?

S

?

?

East Providence, RI

602

3

0

Abbott, Mr. Rossmore Edward

male

16

1

1

C.A. 2673

20.25

?

S

?

190

East Providence, RI

603

3

1

Abbott, Mrs. Stanton (Rosa Hunt)

female

35

1

1

C.A. 2673

20.25

?

S

A

?

East Providence, RI

604

3

1

Abelseth, Miss. Karen Marie

female

16

0

0

348125

7.65

?

S

16

?

Norway Los Angeles, CA

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

392

2

1

del Carlo, Mrs. Sebastiano (Argenia Genovesi)

female

24

1

0

SC/PARIS 2167

27.7208

?

C

12

?

Lucca, Italy / California

1262

3

0

van Billiard, Master. James William

male

?

1

1

A/5. 851

14.5

?

S

?

?

?

1263

3

0

van Billiard, Master. Walter John

male

11.5

1

1

A/5. 851

14.5

?

S

?

1

?

1264

3

0

van Billiard, Mr. Austin Blyler

male

40.5

0

2

A/5. 851

14.5

?

S

?

255

?

1268

3

0

van Melkebeke, Mr. Philemon

male

?

0

0

345777

9.5

?

S

?

?

?

1309 rows × 14 columns

In [35]:

titanic.sort_values("name",key=lambda col: col.str.lower())

Out[35]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

600

3

0

Abbing, Mr. Anthony

male

42

0

0

C.A. 5547

7.55

?

S

?

?

?

601

3

0

Abbott, Master. Eugene Joseph

male

13

0

2

C.A. 2673

20.25

?

S

?

?

East Providence, RI

602

3

0

Abbott, Mr. Rossmore Edward

male

16

1

1

C.A. 2673

20.25

?

S

?

190

East Providence, RI

603

3

1

Abbott, Mrs. Stanton (Rosa Hunt)

female

35

1

1

C.A. 2673

20.25

?

S

A

?

East Providence, RI

604

3

1

Abelseth, Miss. Karen Marie

female

16

0

0

348125

7.65

?

S

16

?

Norway Los Angeles, CA

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

1304

3

0

Zabour, Miss. Hileni

female

14.5

1

0

2665

14.4542

?

C

?

328

?

1305

3

0

Zabour, Miss. Thamine

female

?

1

0

2665

14.4542

?

C

?

?

?

1306

3

0

Zakarian, Mr. Mapriededer

male

26.5

0

0

2656

7.225

?

C

?

304

?

1307

3

0

Zakarian, Mr. Ortin

male

27

0

0

2670

7.225

?

C

?

?

?

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

1309 rows × 14 columns

In [36]:

countries

Out[36]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Singapore

Southeast Asia

6.377

0.043

6.460

6.293

11.488

0.915

76.953

0.927

-0.018

0.082

2.43

1.695

1.019

0.897

0.664

0.176

0.547

1.379

Hong Kong S.A.R. of China

East Asia

5.477

0.049

5.573

5.380

11.000

0.836

76.820

0.717

0.067

0.403

2.43

1.525

0.841

0.893

0.408

0.232

0.342

1.236

Japan

East Asia

5.940

0.040

6.020

5.861

10.611

0.884

75.100

0.796

-0.258

0.638

2.43

1.389

0.949

0.838

0.504

0.020

0.192

2.048

Spain

Western Europe

6.491

0.042

6.574

6.408

10.571

0.932

74.700

0.761

-0.081

0.745

2.43

1.375

1.057

0.826

0.462

0.135

0.124

2.513

Switzerland

Western Europe

7.571

0.036

7.643

7.500

11.117

0.942

74.400

0.919

0.025

0.292

2.43

1.566

1.079

0.816

0.653

0.204

0.413

2.839

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Swaziland

Sub-Saharan Africa

4.308

0.071

4.448

4.168

9.065

0.770

50.833

0.647

-0.185

0.708

2.43

0.849

0.693

0.074

0.323

0.067

0.147

2.155

Ivory Coast

Sub-Saharan Africa

5.306

0.078

5.460

5.152

8.551

0.644

50.114

0.741

-0.016

0.794

2.43

0.669

0.409

0.052

0.438

0.177

0.092

3.469

Nigeria

Sub-Saharan Africa

4.759

0.052

4.861

4.658

8.533

0.740

50.102

0.737

0.037

0.878

2.43

0.663

0.625

0.051

0.433

0.212

0.039

2.736

Lesotho

Sub-Saharan Africa

3.512

0.120

3.748

3.276

7.926

0.787

48.700

0.715

-0.131

0.915

2.43

0.451

0.731

0.007

0.405

0.103

0.015

1.800

Chad

Sub-Saharan Africa

4.355

0.094

4.540

4.171

7.364

0.619

48.478

0.579

0.041

0.807

2.43

0.255

0.353

0.000

0.240

0.215

0.084

3.209

149 rows × 19 columns

In [37]:

countries.sort_index()

Out[37]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

Albania

Central and Eastern Europe

5.117

0.059

5.234

5.001

9.520

0.697

68.999

0.785

-0.030

0.901

2.43

1.008

0.529

0.646

0.491

0.168

0.024

2.250

Algeria

Middle East and North Africa

4.887

0.053

4.991

4.783

9.342

0.802

66.005

0.480

-0.067

0.752

2.43

0.946

0.765

0.552

0.119

0.144

0.120

2.242

Argentina

Latin America and Caribbean

5.929

0.056

6.040

5.819

9.962

0.898

69.000

0.828

-0.182

0.834

2.43

1.162

0.980

0.646

0.544

0.069

0.067

2.461

Armenia

Commonwealth of Independent States

5.283

0.058

5.397

5.168

9.487

0.799

67.055

0.825

-0.168

0.629

2.43

0.996

0.758

0.585

0.540

0.079

0.198

2.127

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Venezuela

Latin America and Caribbean

4.892

0.064

5.017

4.767

9.073

0.861

66.700

0.615

-0.169

0.827

2.43

0.852

0.897

0.574

0.284

0.078

0.072

2.135

Vietnam

Southeast Asia

5.411

0.039

5.488

5.334

8.973

0.850

68.034

0.940

-0.098

0.796

2.43

0.817

0.873

0.616

0.679

0.124

0.091

2.211

Yemen

Middle East and North Africa

3.658

0.070

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.800

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

Zambia

Sub-Saharan Africa

4.073

0.069

4.209

3.938

8.145

0.708

55.809

0.782

0.061

0.823

2.43

0.528

0.552

0.231

0.487

0.227

0.074

1.975

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

149 rows × 19 columns

In [38]:

countries.sort_index(ascending=False, inplace=True)

In [39]:

countries

Out[39]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Zambia

Sub-Saharan Africa

4.073

0.069

4.209

3.938

8.145

0.708

55.809

0.782

0.061

0.823

2.43

0.528

0.552

0.231

0.487

0.227

0.074

1.975

Yemen

Middle East and North Africa

3.658

0.070

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.800

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

Vietnam

Southeast Asia

5.411

0.039

5.488

5.334

8.973

0.850

68.034

0.940

-0.098

0.796

2.43

0.817

0.873

0.616

0.679

0.124

0.091

2.211

Venezuela

Latin America and Caribbean

4.892

0.064

5.017

4.767

9.073

0.861

66.700

0.615

-0.169

0.827

2.43

0.852

0.897

0.574

0.284

0.078

0.072

2.135

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Armenia

Commonwealth of Independent States

5.283

0.058

5.397

5.168

9.487

0.799

67.055

0.825

-0.168

0.629

2.43

0.996

0.758

0.585

0.540

0.079

0.198

2.127

Argentina

Latin America and Caribbean

5.929

0.056

6.040

5.819

9.962

0.898

69.000

0.828

-0.182

0.834

2.43

1.162

0.980

0.646

0.544

0.069

0.067

2.461

Algeria

Middle East and North Africa

4.887

0.053

4.991

4.783

9.342

0.802

66.005

0.480

-0.067

0.752

2.43

0.946

0.765

0.552

0.119

0.144

0.120

2.242

Albania

Central and Eastern Europe

5.117

0.059

5.234

5.001

9.520

0.697

68.999

0.785

-0.030

0.901

2.43

1.008

0.529

0.646

0.491

0.168

0.024

2.250

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

149 rows × 19 columns

In [40]:

titanic.sort_index(ascending=False)

Out[40]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

1308

3

0

Zimmerman, Mr. Leo

male

29

0

0

315082

7.875

?

S

?

?

?

1307

3

0

Zakarian, Mr. Ortin

male

27

0

0

2670

7.225

?

C

?

?

?

1306

3

0

Zakarian, Mr. Mapriededer

male

26.5

0

0

2656

7.225

?

C

?

304

?

1305

3

0

Zabour, Miss. Thamine

female

?

1

0

2665

14.4542

?

C

?

?

?

1304

3

0

Zabour, Miss. Hileni

female

14.5

1

0

2665

14.4542

?

C

?

328

?

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

4

1

0

Allison, Mrs. Hudson J C (Bessie Waldo Daniels)

female

25

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

3

1

0

Allison, Mr. Hudson Joshua Creighton

male

30

1

2

113781

151.55

C22 C26

S

?

135

Montreal, PQ / Chesterville, ON

2

1

0

Allison, Miss. Helen Loraine

female

2

1

2

113781

151.55

C22 C26

S

?

?

Montreal, PQ / Chesterville, ON

1

1

1

Allison, Master. Hudson Trevor

male

0.9167

1

2

113781

151.55

C22 C26

S

11

?

Montreal, PQ / Chesterville, ON

0

1

1

Allen, Miss. Elisabeth Walton

female

29

0

0

24160

211.3375

B5

S

2

?

St Louis, MO

1309 rows × 14 columns

In [41]:

titanic.pclass.value_counts().sort_values()

Out[41]:

2    277

1    323

3    709

Name: pclass, dtype: int64

In [42]:

titanic.pclass.value_counts().plot(kind="bar")

Out[42]:

<AxesSubplot:>

 

In [43]:

titanic.pclass.value_counts().sort_values().plot(kind="bar")

Out[43]:

<AxesSubplot:>

 

In [44]:

titanic.pclass.value_counts().sort_index().plot(kind="bar")

Out[44]:

<AxesSubplot:>

 

In [45]:

houses.bedrooms.value_counts().sort_values().plot(kind="bar")

Out[45]:

<AxesSubplot:>

 

In [46]:

houses.bedrooms.value_counts().sort_index().plot(kind="bar")

Out[46]:

<AxesSubplot:>

 

In [47]:

countries.head()

Out[47]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

Zambia

Sub-Saharan Africa

4.073

0.069

4.209

3.938

8.145

0.708

55.809

0.782

0.061

0.823

2.43

0.528

0.552

0.231

0.487

0.227

0.074

1.975

Yemen

Middle East and North Africa

3.658

0.070

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.800

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

Vietnam

Southeast Asia

5.411

0.039

5.488

5.334

8.973

0.850

68.034

0.940

-0.098

0.796

2.43

0.817

0.873

0.616

0.679

0.124

0.091

2.211

Venezuela

Latin America and Caribbean

4.892

0.064

5.017

4.767

9.073

0.861

66.700

0.615

-0.169

0.827

2.43

0.852

0.897

0.574

0.284

0.078

0.072

2.135

In [74]:

countries["Ladder score"]

Out[74]:

Country name

Afghanistan    2.523

Albania        5.117

Algeria        4.887

Argentina      5.929

Armenia        5.283

               ...  

Venezuela      4.892

Vietnam        5.411

Yemen          3.658

Zambia         4.073

Zimbabwe       3.145

Name: Ladder score, Length: 149, dtype: float64

loc[] & iloc[]

In [49]:

countries.loc["Yemen"]

Out[49]:

Regional indicator                            Middle East and North Africa

Ladder score                                                         3.658

Standard error of ladder score                                        0.07

upperwhisker                                                         3.794

lowerwhisker                                                         3.521

Logged GDP per capita                                                7.578

Social support                                                       0.832

Healthy life expectancy                                             57.122

Freedom to make life choices                                         0.602

Generosity                                                          -0.147

Perceptions of corruption                                              0.8

Ladder score in Dystopia                                              2.43

Explained by: Log GDP per capita                                     0.329

Explained by: Social support                                         0.831

Explained by: Healthy life expectancy                                0.272

Explained by: Freedom to make life choices                           0.268

Explained by: Generosity                                             0.092

Explained by: Perceptions of corruption                              0.089

Dystopia + residual                                                  1.776

Name: Yemen, dtype: object

In [50]:

countries.loc[["Yemen"]]

Out[50]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Yemen

Middle East and North Africa

3.658

0.07

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.8

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

In [51]:

countries.loc[["Canada"]]

Out[51]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Canada

North America and ANZ

7.103

0.042

7.185

7.021

10.776

0.926

73.8

0.915

0.089

0.415

2.43

1.447

1.044

0.798

0.648

0.246

0.335

2.585

In [52]:

countries.loc[["Canada", "Mexico", "United States"]]

Out[52]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Canada

North America and ANZ

7.103

0.042

7.185

7.021

10.776

0.926

73.800

0.915

0.089

0.415

2.43

1.447

1.044

0.798

0.648

0.246

0.335

2.585

Mexico

Latin America and Caribbean

6.317

0.053

6.420

6.213

9.859

0.831

68.597

0.862

-0.147

0.799

2.43

1.126

0.830

0.634

0.585

0.092

0.089

2.961

United States

North America and ANZ

6.951

0.049

7.047

6.856

11.023

0.920

68.200

0.837

0.098

0.698

2.43

1.533

1.030

0.621

0.554

0.252

0.154

2.807

In [53]:

titanic.loc[[7,9,876]]

Out[53]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

7

1

0

Andrews, Mr. Thomas Jr

male

39

0

0

112050

0

A36

S

?

?

Belfast, NI

9

1

0

Artagaveytia, Mr. Ramon

male

71

0

0

PC 17609

49.5042

?

C

?

22

Montevideo, Uruguay

876

3

0

Ilieff, Mr. Ylio

male

?

0

0

349220

7.8958

?

S

?

?

?

In [54]:

titanic.loc[5:10]

Out[54]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

5

1

1

Anderson, Mr. Harry

male

48

0

0

19952

26.55

E12

S

3

?

New York, NY

6

1

1

Andrews, Miss. Kornelia Theodosia

female

63

1

0

13502

77.9583

D7

S

10

?

Hudson, NY

7

1

0

Andrews, Mr. Thomas Jr

male

39

0

0

112050

0

A36

S

?

?

Belfast, NI

8

1

1

Appleton, Mrs. Edward Dale (Charlotte Lamson)

female

53

2

0

11769

51.4792

C101

S

D

?

Bayside, Queens, NY

9

1

0

Artagaveytia, Mr. Ramon

male

71

0

0

PC 17609

49.5042

?

C

?

22

Montevideo, Uruguay

10

1

0

Astor, Col. John Jacob

male

47

1

0

PC 17757

227.525

C62 C64

C

?

124

New York, NY

In [55]:

titanic.loc[5:10:2]

Out[55]:

 

pclass

survived

name

sex

age

sibsp

parch

ticket

fare

cabin

embarked

boat

body

home.dest

5

1

1

Anderson, Mr. Harry

male

48

0

0

19952

26.55

E12

S

3

?

New York, NY

7

1

0

Andrews, Mr. Thomas Jr

male

39

0

0

112050

0

A36

S

?

?

Belfast, NI

9

1

0

Artagaveytia, Mr. Ramon

male

71

0

0

PC 17609

49.5042

?

C

?

22

Montevideo, Uruguay

In [56]:

countries.sort_index(inplace=True)

In [57]:

countries

Out[57]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

Albania

Central and Eastern Europe

5.117

0.059

5.234

5.001

9.520

0.697

68.999

0.785

-0.030

0.901

2.43

1.008

0.529

0.646

0.491

0.168

0.024

2.250

Algeria

Middle East and North Africa

4.887

0.053

4.991

4.783

9.342

0.802

66.005

0.480

-0.067

0.752

2.43

0.946

0.765

0.552

0.119

0.144

0.120

2.242

Argentina

Latin America and Caribbean

5.929

0.056

6.040

5.819

9.962

0.898

69.000

0.828

-0.182

0.834

2.43

1.162

0.980

0.646

0.544

0.069

0.067

2.461

Armenia

Commonwealth of Independent States

5.283

0.058

5.397

5.168

9.487

0.799

67.055

0.825

-0.168

0.629

2.43

0.996

0.758

0.585

0.540

0.079

0.198

2.127

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Venezuela

Latin America and Caribbean

4.892

0.064

5.017

4.767

9.073

0.861

66.700

0.615

-0.169

0.827

2.43

0.852

0.897

0.574

0.284

0.078

0.072

2.135

Vietnam

Southeast Asia

5.411

0.039

5.488

5.334

8.973

0.850

68.034

0.940

-0.098

0.796

2.43

0.817

0.873

0.616

0.679

0.124

0.091

2.211

Yemen

Middle East and North Africa

3.658

0.070

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.800

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

Zambia

Sub-Saharan Africa

4.073

0.069

4.209

3.938

8.145

0.708

55.809

0.782

0.061

0.823

2.43

0.528

0.552

0.231

0.487

0.227

0.074

1.975

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

149 rows × 19 columns

In [58]:

countries.loc["Denmark":"France"]

Out[58]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Denmark

Western Europe

7.620

0.035

7.687

7.552

10.933

0.954

72.700

0.946

0.030

0.179

2.43

1.502

1.108

0.763

0.686

0.208

0.485

2.868

Dominican Republic

Latin America and Caribbean

5.545

0.071

5.685

5.405

9.802

0.853

66.102

0.860

-0.133

0.714

2.43

1.106

0.879

0.555

0.581

0.101

0.144

2.178

Ecuador

Latin America and Caribbean

5.764

0.057

5.875

5.653

9.313

0.821

68.800

0.842

-0.124

0.843

2.43

0.935

0.806

0.640

0.560

0.107

0.062

2.653

Egypt

Middle East and North Africa

4.283

0.045

4.371

4.195

9.367

0.750

61.998

0.749

-0.182

0.795

2.43

0.954

0.647

0.426

0.446

0.069

0.092

1.648

El Salvador

Latin America and Caribbean

6.061

0.065

6.188

5.933

9.054

0.762

66.402

0.888

-0.110

0.688

2.43

0.845

0.675

0.565

0.615

0.116

0.160

3.085

Estonia

Central and Eastern Europe

6.189

0.038

6.263

6.115

10.481

0.941

68.800

0.909

-0.106

0.527

2.43

1.344

1.079

0.640

0.641

0.119

0.263

2.103

Ethiopia

Sub-Saharan Africa

4.275

0.051

4.374

4.175

7.694

0.764

59.000

0.752

0.082

0.761

2.43

0.370

0.679

0.331

0.451

0.241

0.114

2.089

Finland

Western Europe

7.842

0.032

7.904

7.780

10.775

0.954

72.000

0.949

-0.098

0.186

2.43

1.446

1.106

0.741

0.691

0.124

0.481

3.253

France

Western Europe

6.690

0.037

6.762

6.618

10.704

0.942

74.000

0.822

-0.147

0.571

2.43

1.421

1.081

0.804

0.536

0.092

0.235

2.521

In [59]:

countries

Out[59]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.370

0.000

0.126

0.000

0.122

0.010

1.895

Albania

Central and Eastern Europe

5.117

0.059

5.234

5.001

9.520

0.697

68.999

0.785

-0.030

0.901

2.43

1.008

0.529

0.646

0.491

0.168

0.024

2.250

Algeria

Middle East and North Africa

4.887

0.053

4.991

4.783

9.342

0.802

66.005

0.480

-0.067

0.752

2.43

0.946

0.765

0.552

0.119

0.144

0.120

2.242

Argentina

Latin America and Caribbean

5.929

0.056

6.040

5.819

9.962

0.898

69.000

0.828

-0.182

0.834

2.43

1.162

0.980

0.646

0.544

0.069

0.067

2.461

Armenia

Commonwealth of Independent States

5.283

0.058

5.397

5.168

9.487

0.799

67.055

0.825

-0.168

0.629

2.43

0.996

0.758

0.585

0.540

0.079

0.198

2.127

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

Venezuela

Latin America and Caribbean

4.892

0.064

5.017

4.767

9.073

0.861

66.700

0.615

-0.169

0.827

2.43

0.852

0.897

0.574

0.284

0.078

0.072

2.135

Vietnam

Southeast Asia

5.411

0.039

5.488

5.334

8.973

0.850

68.034

0.940

-0.098

0.796

2.43

0.817

0.873

0.616

0.679

0.124

0.091

2.211

Yemen

Middle East and North Africa

3.658

0.070

3.794

3.521

7.578

0.832

57.122

0.602

-0.147

0.800

2.43

0.329

0.831

0.272

0.268

0.092

0.089

1.776

Zambia

Sub-Saharan Africa

4.073

0.069

4.209

3.938

8.145

0.708

55.809

0.782

0.061

0.823

2.43

0.528

0.552

0.231

0.487

0.227

0.074

1.975

Zimbabwe

Sub-Saharan Africa

3.145

0.058

3.259

3.030

7.943

0.750

56.201

0.677

-0.047

0.821

2.43

0.457

0.649

0.243

0.359

0.157

0.075

1.205

149 rows × 19 columns

In [60]:

countries.iloc[[0]]

Out[60]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Afghanistan

South Asia

2.523

0.038

2.596

2.449

7.695

0.463

52.493

0.382

-0.102

0.924

2.43

0.37

0.0

0.126

0.0

0.122

0.01

1.895

In [61]:

countries.iloc[[10,20,30,100]]

Out[61]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Belarus

Commonwealth of Independent States

5.534

0.047

5.625

5.442

9.853

0.910

66.253

0.650

-0.180

0.627

2.43

1.124

1.007

0.560

0.326

0.070

0.199

2.247

Cambodia

Southeast Asia

4.830

0.067

4.963

4.698

8.360

0.765

62.000

0.959

0.034

0.843

2.43

0.603

0.680

0.426

0.702

0.210

0.061

2.148

Croatia

Central and Eastern Europe

5.882

0.048

5.975

5.788

10.217

0.924

70.799

0.754

-0.118

0.939

2.43

1.251

1.039

0.703

0.453

0.111

0.000

2.325

Nigeria

Sub-Saharan Africa

4.759

0.052

4.861

4.658

8.533

0.740

50.102

0.737

0.037

0.878

2.43

0.663

0.625

0.051

0.433

0.212

0.039

2.736

In [62]:

countries.iloc[50:60]

Out[62]:

 

Regional indicator

Ladder score

Standard error of ladder score

upperwhisker

lowerwhisker

Logged GDP per capita

Social support

Healthy life expectancy

Freedom to make life choices

Generosity

Perceptions of corruption

Ladder score in Dystopia

Explained by: Log GDP per capita

Explained by: Social support

Explained by: Healthy life expectancy

Explained by: Freedom to make life choices

Explained by: Generosity

Explained by: Perceptions of corruption

Dystopia + residual

Country name

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Haiti

Latin America and Caribbean

3.615

0.173

3.953

3.276

7.477

0.540

55.700

0.593

0.422

0.721

2.43

0.294

0.173

0.227

0.257

0.463

0.139

2.060

Honduras

Latin America and Caribbean

5.919

0.082

6.081

5.758

8.648

0.812

67.300

0.857

0.081

0.809

2.43

0.703

0.787

0.593

0.578

0.241

0.083

2.934

Hong Kong S.A.R. of China

East Asia

5.477

0.049

5.573

5.380

11.000

0.836

76.820

0.717

0.067

0.403

2.43

1.525

0.841

0.893

0.408

0.232

0.342

1.236

Hungary

Central and Eastern Europe

5.992

0.047

6.085

5.899

10.358

0.943

68.000

0.755

-0.186

0.876

2.43

1.301

1.083

0.615

0.454

0.067

0.040

2.432

Iceland

Western Europe

7.554

0.059

7.670

7.438

10.878

0.983

73.000

0.955

0.160

0.673

2.43

1.482

1.172

0.772

0.698

0.293

0.170

2.967

India

South Asia

3.819

0.026

3.869

3.769

8.755

0.603

60.633

0.893

0.089

0.774

2.43

0.741

0.316

0.383

0.622

0.246

0.106

1.405

Indonesia

Southeast Asia

5.345

0.056

5.454

5.235

9.365

0.811

62.236

0.873

0.542

0.867

2.43

0.954

0.786

0.433

0.598

0.541

0.046

1.987

Iran

Middle East and North Africa

4.721

0.055

4.828

4.614

9.584

0.710

66.300

0.608

0.218

0.714

2.43

1.030

0.557

0.561

0.275

0.330

0.144

1.823

Iraq

Middle East and North Africa

4.854

0.059

4.970

4.738

9.240

0.746

60.583

0.630

-0.053

0.875

2.43

0.910

0.638

0.381

0.302

0.153

0.041

2.429

Ireland

Western Europe

7.085

0.040

7.164

7.006

11.342

0.947

72.400

0.879

0.077

0.363

2.43

1.644

1.092

0.753

0.606

0.238

0.367

2.384

In [63]:

houses.sort_index(ascending=False, inplace=True)

In [64]:

houses

Out[64]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

21612

1523300157

20141015T000000

325000.0

2

0.75

1020

1076

2.0

0

0

...

7

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

21611

291310100

20150116T000000

400000.0

3

2.50

1600

2388

2.0

0

0

...

8

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

21610

1523300141

20140623T000000

402101.0

2

0.75

1020

1350

2.0

0

0

...

7

1020

0

2009

0

98144

47.5944

-122.299

1020

2007

21609

6600060120

20150223T000000

400000.0

4

2.50

2310

5813

2.0

0

0

...

8

2310

0

2014

0

98146

47.5107

-122.362

1830

7200

21608

263000018

20140521T000000

360000.0

3

2.50

1530

1131

3.0

0

0

...

8

1530

0

2009

0

98103

47.6993

-122.346

1530

1509

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

...

4

1954400510

20150218T000000

510000.0

3

2.00

1680

8080

1.0

0

0

...

8

1680

0

1987

0

98074

47.6168

-122.045

1800

7503

3

2487200875

20141209T000000

604000.0

4

3.00

1960

5000

1.0

0

0

...

7

1050

910

1965

0

98136

47.5208

-122.393

1360

5000

2

5631500400

20150225T000000

180000.0

2

1.00

770

10000

1.0

0

0

...

6

770

0

1933

0

98028

47.7379

-122.233

2720

8062

1

6414100192

20141209T000000

538000.0

3

2.25

2570

7242

2.0

0

0

...

7

2170

400

1951

1991

98125

47.7210

-122.319

1690

7639

0

7129300520

20141013T000000

221900.0

3

1.00

1180

5650

1.0

0

0

...

7

1180

0

1955

0

98178

47.5112

-122.257

1340

5650

21613 rows × 21 columns

In [65]:

houses.iloc[0:5]

Out[65]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

21612

1523300157

20141015T000000

325000.0

2

0.75

1020

1076

2.0

0

0

...

7

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

21611

291310100

20150116T000000

400000.0

3

2.50

1600

2388

2.0

0

0

...

8

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

21610

1523300141

20140623T000000

402101.0

2

0.75

1020

1350

2.0

0

0

...

7

1020

0

2009

0

98144

47.5944

-122.299

1020

2007

21609

6600060120

20150223T000000

400000.0

4

2.50

2310

5813

2.0

0

0

...

8

2310

0

2014

0

98146

47.5107

-122.362

1830

7200

21608

263000018

20140521T000000

360000.0

3

2.50

1530

1131

3.0

0

0

...

8

1530

0

2009

0

98103

47.6993

-122.346

1530

1509

5 rows × 21 columns

In [66]:

houses.loc[21612:21611]

Out[66]:

 

id

date

price

bedrooms

bathrooms

sqft_living

sqft_lot

floors

waterfront

view

...

grade

sqft_above

sqft_basement

yr_built

yr_renovated

zipcode

lat

long

sqft_living15

sqft_lot15

21612

1523300157

20141015T000000

325000.0

2

0.75

1020

1076

2.0

0

0

...

7

1020

0

2008

0

98144

47.5941

-122.299

1020

1357

21611

291310100

20150116T000000

400000.0

3

2.50

1600

2388

2.0

0

0

...

8

1600

0

2004

0

98027

47.5345

-122.069

1410

1287

2 rows × 21 columns

In [67]:

houses.loc[21612:21611, ['price', 'bedrooms']]

Out[67]:

 

price

bedrooms

21612

325000.0

2

21611

400000.0

3

In [68]:

titanic.loc[50:60:2, ['name', 'sex', 'age']]

Out[68]:

 

name

sex

age

50

Cardeza, Mrs. James Warburton Martinez (Charlo...

female

58

52

Carrau, Mr. Francisco M

male

28

54

Carter, Master. William Thornton II

male

11

56

Carter, Mr. William Ernest

male

36

58

Case, Mr. Howard Brown

male

49

60

Cavendish, Mr. Tyrell William

male

36

In [69]:

countries.loc["Canada": "Denmark", ['Ladder score']]

Out[69]:

 

Ladder score

Country name

 

Canada

7.103

Chad

4.355

Chile

6.172

China

5.339

Colombia

6.012

Comoros

4.289

Congo (Brazzaville)

5.342

Costa Rica

7.069

Croatia

5.882

Cyprus

6.223

Czech Republic

6.965

Denmark

7.620

In [70]:

houses["bedrooms"].value_counts().loc[33]

Out[70]:

1

In [71]:

titanic["age"].value_counts().loc["18"]

Out[71]:

39

In [72]:

titanic["age"].value_counts().iloc[0:5]

Out[72]:

?     263

24     47

22     43

21     41

30     40

Name: age, dtype: int64